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Handbook of statistical systems biology [[electronic resource] /] / edited by Michael P.H. Stumpf, David J. Balding, Mark Girolami
| Handbook of statistical systems biology [[electronic resource] /] / edited by Michael P.H. Stumpf, David J. Balding, Mark Girolami |
| Pubbl/distr/stampa | Chichester, West Sussex ; ; Hoboken, N.J., : John Wiley & Sons, 2011 |
| Descrizione fisica | 1 online resource (532 p.) |
| Disciplina | 570.1/5195 |
| Altri autori (Persone) |
StumpfM. P. H (Michael P. H.)
BaldingD. J GirolamiMark <1963-> |
| Soggetto topico |
Systems biology - Statistical methods
Biological systems - Mathematical models Uncertainty - Mathematical models Stochastic analysis - Mathematical models |
| ISBN |
1-283-25824-2
9786613258243 1-119-95204-2 1-119-97060-1 1-119-97061-X |
| Formato | Materiale a stampa  |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | A. Methodological chapters -- B. Technology-based chapters -- C. Networks and graphical models -- D. Dynamical systems -- E. Application areas. |
| Record Nr. | UNINA-9910139588603321 |
| Chichester, West Sussex ; ; Hoboken, N.J., : John Wiley & Sons, 2011 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Handbook of statistical systems biology / / edited by Michael P.H. Stumpf, David J. Balding, Mark Girolami
| Handbook of statistical systems biology / / edited by Michael P.H. Stumpf, David J. Balding, Mark Girolami |
| Edizione | [1st ed.] |
| Pubbl/distr/stampa | Chichester, West Sussex ; ; Hoboken, N.J., : John Wiley & Sons, 2011 |
| Descrizione fisica | 1 online resource (532 p.) |
| Disciplina | 570.1/5195 |
| Altri autori (Persone) |
StumpfM. P. H (Michael P. H.)
BaldingD. J GirolamiMark <1963-> |
| Soggetto topico |
Systems biology - Statistical methods
Biological systems - Mathematical models Uncertainty - Mathematical models Stochastic analysis - Mathematical models |
| ISBN |
1-283-25824-2
9786613258243 1-119-95204-2 1-119-97060-1 1-119-97061-X |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | A. Methodological chapters -- B. Technology-based chapters -- C. Networks and graphical models -- D. Dynamical systems -- E. Application areas. |
| Record Nr. | UNINA-9910826879403321 |
| Chichester, West Sussex ; ; Hoboken, N.J., : John Wiley & Sons, 2011 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Statistical and evolutionary analysis of biological networks [[electronic resource] /] / editors, Michael P.H. Stumpf, Carsten Wiuf
| Statistical and evolutionary analysis of biological networks [[electronic resource] /] / editors, Michael P.H. Stumpf, Carsten Wiuf |
| Pubbl/distr/stampa | London, : Imperial College Press, c2010 |
| Descrizione fisica | 1 online resource (179 p.) |
| Disciplina | 570.15195 |
| Altri autori (Persone) |
StumpfM. P. H (Michael P. H.)
WiufCarsten |
| Soggetto topico |
Biometry
Computational biology Graph theory |
| Soggetto genere / forma | Electronic books. |
| ISBN |
1-282-75998-1
9786612759987 1-84816-434-3 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Contents; Preface; 1. A Network Analysis Primer Michael P.H. Stumpf and Carsten Wiuf; 1.1. Introduction; 1.2. Types of Biological Networks; 1.3. A Primer on Networks; 1.3.1. Mathematical descriptions of networks; 1.3.1.1. Characteristics of a node; 1.3.1.2. Paths, components and trees; 1.3.1.3. Distance and diameter; 1.3.2. Network properties; 1.3.2.1. The degree distribution; 1.3.2.2. Clustering; 1.3.2.3. Average path length; 1.3.3. Mathematical representation of networks; 1.3.3.1. The adjacency matrix; 1.3.3.2. The adjacency list; 1.3.3.3. The edge list; 1.3.3.4. Some remarks on complexity
1.4. Comparing Biological Networks 1.4.1. Identity of networks; 1.4.2. Subnets and patterns; 1.4.3. The challenges of the data; References; 2. Evolutionary Analysis of Protein Interaction Networks Carsten Wiuf and Oliver Ratmann; 2.1. Introduction; 2.1.1. Molecular genetic uptake; 2.1.2. Expansion by gene duplication; 2.1.3. Redeployment of existing genetic systems; 2.2. Protein Interaction Network Data; 2.3. Mathematical Models of Networks and Network Growth; 2.3.1. Simplistic models of network growth; 2.3.2. Complex models of network growth by repeated node addition 2.3.3. Asymptotics of the node degree DD+RA and DD+PA2. 4. Inferring Evolutionary Dynamics in Terms of Mixture Models of Network Growth; 2.4.1. The likelihood of PIN data under DD+RA or DD+PA; 2.4.2. Simple methods to account for incomplete datasets; 2.4.3. Approximating the likelihood with many summaries; 2.4.4. Approximate Bayesian computation; 2.4.5. Evolutionary analysis of the PIN topologies of T. pallidum, H. pylori and P. falciparum; 2.4.6. The size of the interactome; 2.5. Conclusion; Acknowledgements; Appendix A. Proofs of Theorems.; References 3. Motifs in Biological Networks Falk Schreiber and Henning Schw obbermeyer 3.1. Introduction; 3.2. Characterisation of Network Motifs; 3.2.1. Definitions; 3.2.2. Modelling of biological data as graphs; 3.2.3. Complexity of motif search; 3.2.4. Frequency concepts; 3.2.5. Statistical significance of network motifs; 3.2.6. Randomisation algorithm for generation of null model networks; 3.2.7. Calculation of the P-value and Z-score; 3.3. Methods and Tools for the Analysis of Network Motifs; 3.3.1. Mfinder; 3.3.2. Pajek; 3.3.3. MAVisto; 3.4. Analyses of Motifs in Networks 3.4.1. Analysis of gene regulatory networks 3.4.2. Motifs in cortical networks; 3.4.3. Analysis of other networks; 3.4.4. Superstructures formed by overlapping motif matches; 3.4.5. Dynamic properties of network motifs; 3.4.6. Comparison of networks using motif distributions; 3.4.7. On the function of network motifs in biological networks; References; 4. Bayesian Analysis of Biological Networks: Clusters, Motifs, Cross- Species Correlations Johannes Berg and Michael Lassig; 4.1. Introduction; 4.2. Measuring Biological Networks; 4.3. Random Networks in Biology; 4.4. Network Clusters 4.4.1. Clusters in protein interaction networks |
| Record Nr. | UNINA-9910456109503321 |
| London, : Imperial College Press, c2010 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Statistical and evolutionary analysis of biological networks [[electronic resource] /] / editors, Michael P.H. Stumpf, Carsten Wiuf
| Statistical and evolutionary analysis of biological networks [[electronic resource] /] / editors, Michael P.H. Stumpf, Carsten Wiuf |
| Pubbl/distr/stampa | London, : Imperial College Press, c2010 |
| Descrizione fisica | 1 online resource (179 p.) |
| Disciplina | 570.15195 |
| Altri autori (Persone) |
StumpfM. P. H (Michael P. H.)
WiufCarsten |
| Soggetto topico |
Biometry
Computational biology Graph theory |
| ISBN |
1-282-75998-1
9786612759987 1-84816-434-3 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Contents; Preface; 1. A Network Analysis Primer Michael P.H. Stumpf and Carsten Wiuf; 1.1. Introduction; 1.2. Types of Biological Networks; 1.3. A Primer on Networks; 1.3.1. Mathematical descriptions of networks; 1.3.1.1. Characteristics of a node; 1.3.1.2. Paths, components and trees; 1.3.1.3. Distance and diameter; 1.3.2. Network properties; 1.3.2.1. The degree distribution; 1.3.2.2. Clustering; 1.3.2.3. Average path length; 1.3.3. Mathematical representation of networks; 1.3.3.1. The adjacency matrix; 1.3.3.2. The adjacency list; 1.3.3.3. The edge list; 1.3.3.4. Some remarks on complexity
1.4. Comparing Biological Networks 1.4.1. Identity of networks; 1.4.2. Subnets and patterns; 1.4.3. The challenges of the data; References; 2. Evolutionary Analysis of Protein Interaction Networks Carsten Wiuf and Oliver Ratmann; 2.1. Introduction; 2.1.1. Molecular genetic uptake; 2.1.2. Expansion by gene duplication; 2.1.3. Redeployment of existing genetic systems; 2.2. Protein Interaction Network Data; 2.3. Mathematical Models of Networks and Network Growth; 2.3.1. Simplistic models of network growth; 2.3.2. Complex models of network growth by repeated node addition 2.3.3. Asymptotics of the node degree DD+RA and DD+PA2. 4. Inferring Evolutionary Dynamics in Terms of Mixture Models of Network Growth; 2.4.1. The likelihood of PIN data under DD+RA or DD+PA; 2.4.2. Simple methods to account for incomplete datasets; 2.4.3. Approximating the likelihood with many summaries; 2.4.4. Approximate Bayesian computation; 2.4.5. Evolutionary analysis of the PIN topologies of T. pallidum, H. pylori and P. falciparum; 2.4.6. The size of the interactome; 2.5. Conclusion; Acknowledgements; Appendix A. Proofs of Theorems.; References 3. Motifs in Biological Networks Falk Schreiber and Henning Schw obbermeyer 3.1. Introduction; 3.2. Characterisation of Network Motifs; 3.2.1. Definitions; 3.2.2. Modelling of biological data as graphs; 3.2.3. Complexity of motif search; 3.2.4. Frequency concepts; 3.2.5. Statistical significance of network motifs; 3.2.6. Randomisation algorithm for generation of null model networks; 3.2.7. Calculation of the P-value and Z-score; 3.3. Methods and Tools for the Analysis of Network Motifs; 3.3.1. Mfinder; 3.3.2. Pajek; 3.3.3. MAVisto; 3.4. Analyses of Motifs in Networks 3.4.1. Analysis of gene regulatory networks 3.4.2. Motifs in cortical networks; 3.4.3. Analysis of other networks; 3.4.4. Superstructures formed by overlapping motif matches; 3.4.5. Dynamic properties of network motifs; 3.4.6. Comparison of networks using motif distributions; 3.4.7. On the function of network motifs in biological networks; References; 4. Bayesian Analysis of Biological Networks: Clusters, Motifs, Cross- Species Correlations Johannes Berg and Michael Lassig; 4.1. Introduction; 4.2. Measuring Biological Networks; 4.3. Random Networks in Biology; 4.4. Network Clusters 4.4.1. Clusters in protein interaction networks |
| Record Nr. | UNINA-9910780891503321 |
| London, : Imperial College Press, c2010 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Statistical and evolutionary analysis of biological networks / / editors, Michael P.H. Stumpf, Carsten Wiuf
| Statistical and evolutionary analysis of biological networks / / editors, Michael P.H. Stumpf, Carsten Wiuf |
| Edizione | [1st ed.] |
| Pubbl/distr/stampa | London, : Imperial College Press, c2010 |
| Descrizione fisica | 1 online resource (179 p.) |
| Disciplina | 570.15195 |
| Altri autori (Persone) |
StumpfM. P. H (Michael P. H.)
WiufCarsten |
| Soggetto topico |
Biometry
Computational biology Graph theory |
| ISBN |
1-282-75998-1
9786612759987 1-84816-434-3 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Contents; Preface; 1. A Network Analysis Primer Michael P.H. Stumpf and Carsten Wiuf; 1.1. Introduction; 1.2. Types of Biological Networks; 1.3. A Primer on Networks; 1.3.1. Mathematical descriptions of networks; 1.3.1.1. Characteristics of a node; 1.3.1.2. Paths, components and trees; 1.3.1.3. Distance and diameter; 1.3.2. Network properties; 1.3.2.1. The degree distribution; 1.3.2.2. Clustering; 1.3.2.3. Average path length; 1.3.3. Mathematical representation of networks; 1.3.3.1. The adjacency matrix; 1.3.3.2. The adjacency list; 1.3.3.3. The edge list; 1.3.3.4. Some remarks on complexity
1.4. Comparing Biological Networks 1.4.1. Identity of networks; 1.4.2. Subnets and patterns; 1.4.3. The challenges of the data; References; 2. Evolutionary Analysis of Protein Interaction Networks Carsten Wiuf and Oliver Ratmann; 2.1. Introduction; 2.1.1. Molecular genetic uptake; 2.1.2. Expansion by gene duplication; 2.1.3. Redeployment of existing genetic systems; 2.2. Protein Interaction Network Data; 2.3. Mathematical Models of Networks and Network Growth; 2.3.1. Simplistic models of network growth; 2.3.2. Complex models of network growth by repeated node addition 2.3.3. Asymptotics of the node degree DD+RA and DD+PA2. 4. Inferring Evolutionary Dynamics in Terms of Mixture Models of Network Growth; 2.4.1. The likelihood of PIN data under DD+RA or DD+PA; 2.4.2. Simple methods to account for incomplete datasets; 2.4.3. Approximating the likelihood with many summaries; 2.4.4. Approximate Bayesian computation; 2.4.5. Evolutionary analysis of the PIN topologies of T. pallidum, H. pylori and P. falciparum; 2.4.6. The size of the interactome; 2.5. Conclusion; Acknowledgements; Appendix A. Proofs of Theorems.; References 3. Motifs in Biological Networks Falk Schreiber and Henning Schw obbermeyer 3.1. Introduction; 3.2. Characterisation of Network Motifs; 3.2.1. Definitions; 3.2.2. Modelling of biological data as graphs; 3.2.3. Complexity of motif search; 3.2.4. Frequency concepts; 3.2.5. Statistical significance of network motifs; 3.2.6. Randomisation algorithm for generation of null model networks; 3.2.7. Calculation of the P-value and Z-score; 3.3. Methods and Tools for the Analysis of Network Motifs; 3.3.1. Mfinder; 3.3.2. Pajek; 3.3.3. MAVisto; 3.4. Analyses of Motifs in Networks 3.4.1. Analysis of gene regulatory networks 3.4.2. Motifs in cortical networks; 3.4.3. Analysis of other networks; 3.4.4. Superstructures formed by overlapping motif matches; 3.4.5. Dynamic properties of network motifs; 3.4.6. Comparison of networks using motif distributions; 3.4.7. On the function of network motifs in biological networks; References; 4. Bayesian Analysis of Biological Networks: Clusters, Motifs, Cross- Species Correlations Johannes Berg and Michael Lassig; 4.1. Introduction; 4.2. Measuring Biological Networks; 4.3. Random Networks in Biology; 4.4. Network Clusters 4.4.1. Clusters in protein interaction networks |
| Record Nr. | UNINA-9910816637803321 |
| London, : Imperial College Press, c2010 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||